A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation
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| Publicado en: | Water Resources Management vol. 36, no. 9 (Jul 2022), p. 3207 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
Springer Nature B.V.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 0920-4741 | ||
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| 024 | 7 | |a 10.1007/s11269-022-03196-z |2 doi | |
| 035 | |a 2692887035 | ||
| 045 | 2 | |b d20220701 |b d20220731 | |
| 084 | |a 108395 |2 nlm | ||
| 100 | 1 | |a Yang, Rui |u Xidian University, School of Computer Science and Technology, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) | |
| 245 | 1 | |a A Parallel Multi-objective Optimization Algorithm Based on Coarse-to-Fine Decomposition for Real-time Large-scale Reservoir Flood Control Operation | |
| 260 | |b Springer Nature B.V. |c Jul 2022 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Reservoir flood control operation (RFCO) is a multi-objective optimization problem with a long sequence of correlated decision variables. It brings big challenges to large-scale multi-objective optimizers which were generally developed based on the divide-and-conquer strategy. For solving large-scale RFCO problem, a novel coarse-to-fine decomposition method is developed and combined with the algorithmic framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), giving rise to the proposed pCFD-MOEA/D algorithm. The pCFD-MOEA/D algorithm first divides the original RFCO problem into a sequence of sub-problems from coarse to fine scale with different scheduling time intervals. Then all sub-problems are optimized simultaneously and communicate at set intervals. Experimental results on three typical floods at Ankang reservoir have demonstrated that the proposed pCFD-MOEA/D can successfully obtain the elaborate hourly schedule schemes in real time and outperforms the compared algorithms. | |
| 653 | |a Scheduling | ||
| 653 | |a Algorithms | ||
| 653 | |a Flood control | ||
| 653 | |a Optimization | ||
| 653 | |a Decomposition | ||
| 653 | |a Reservoirs | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Real time | ||
| 653 | |a Sequencing | ||
| 653 | |a Intervals | ||
| 653 | |a Floods | ||
| 653 | |a Evolutionary algorithms | ||
| 653 | |a Dams | ||
| 653 | |a Computer science | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Variables | ||
| 653 | |a Linear programming | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Qi, Yutao |u Xidian University, School of Cyber Engineering, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) | |
| 700 | 1 | |a Lei, Jiaojiao |u Xidian University, School of Computer Science and Technology, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) | |
| 700 | 1 | |a Ma, Xiaoliang |u Shenzhen University, College of Computer Science and Software Engineering, Shenzhen, China (GRID:grid.263488.3) (ISNI:0000 0001 0472 9649) | |
| 700 | 1 | |a Zhang, Haibin |u Xidian University, School of Cyber Engineering, Xi’an, China (GRID:grid.440736.2) (ISNI:0000 0001 0707 115X) | |
| 773 | 0 | |t Water Resources Management |g vol. 36, no. 9 (Jul 2022), p. 3207 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2692887035/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2692887035/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |